PROJECT SUMMARY Visual sensation and computation begin in the retina, and retinal defects are the leading causes of vision loss in the developed world. Accordingly, the retina has been studied in detail. Over the past decade, the mouse has emerged as a favored model for retinal analysis, thanks to its ready availability, the vast array of molecular and genetic tools available, and the central position of mouse models in biomedicine generally. Exploiting these strengths, several groups, including ours, have identified genes selectively expressed by many of the >100 retinal cell types (grouped into 5 neuronal classes and several non-neuronal cell types), and such studies have led to deep insights into the structure, function and development of retinal circuits. Unfortunately, however, the mouse has a serious limitation as a model of human retinal function and dysfunction. Most visual perception in humans and other primates relies on a central specialization called the fovea; other mammals, including mice, lack this structure. Thus, diseases that affect the fovea?such as age-related macular degeneration, which is a major cause of blindness?cannot be modeled in the mouse. Yet, research on the primate retina lags behind that of the mouse retina, and molecular studies of the primate retina lag further still. We propose to help bridge this gap in knowledge by classifying the cell types that comprise the foveal and peripheral retina of macaques, and determining the gene expression patterns that define each type. To this end, we will use high-throughput, single-cell RNA sequencing (scRNAseq) methods, which allow profiling of thousands of cells at moderate cost. We helped develop one such method, called Drop-seq, and combined it with computational clustering to analyze >80,000 cells from the mouse retina; this effort resulted in molecular profiles for most cell types and an optimized, scalable pipeline for future studies. This pipeline is well suited for tissue that is difficult to obtain and for small samples such as those from the fovea. As a first step, we will perform scRNAseq on 50,000 cells each from foveal and peripheral retina, then use our bioinformatic methods to divide their profiles into groups. We will then make a tentative identification of these groups with cell types, based on expression of orthologous genes obtained from a taxonomy of retinal cell types in mouse that is now nearing completion in our hands. We will then turn to a comprehensive classification of retinal ganglion cells, which we will purify from foveal and peripheral retina using a cell-sorting protocol that we developed for mouse and have now adapted to macaque. Finally, we will develop methods for relating gene expression patterns of macaque retinal neurons to their morphology, providing protocols that can be used for detailed characterization in the following years. Taken together, our proposed experiments constitute early steps toward a complete identification of cell types in the primate retina; this, in turn, will generate new insights into foveal specializations, and lay the foundation for analysis of normal and diseased human retina.